Abstract 4141821: Genotype and Family History as Risk Markers of Sudden Cardiac Death in Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
Introduction: The role of genotype in sudden cardiac death (SCD) risk stratification in hypertrophic cardiomyopathy (HCM) patients is unclear. Whether family history of SCD (FHxSCD) is independently associated with SCD after correction for genotype has never been investigated. Methods: This observational cohort study included HCM patients who had genetic testing at Toronto General Hospital or Tufts Medical Center. FHxSCD was defined as unexplained SCD before the age of 40 or due to HCM in a first-degree family member. SARC+ was defined as the identification of a (likely) pathogenic variant in a sarcomeric gene. The combined SCD outcome included SCD, resuscitated cardiac arrest (rCA), or appropriate ICD therapy. Results: The study included 3258 patients [2142 (65.7%) male, 896 (27.5%) SARC+]. Over a median follow-up of 5 years (IQR 1.9–9.6), 114 (3.5%) experienced a SCD outcome (10 SCD, 27 rCA, and 77 ICD therapies). Both FHxSCD and SARC+ were independently associated with SCD, with hazard ratios of 1.97 [1.26-3.10, P = 0.003] and 1.73 [1.15-2.59, P = 0.008], respectively (Fig. 1). Among patients with FHxSCD but no other risk markers, the cumulative proportion of SCD was 7.8% and 9.8% at 5 and 10 years in SARC+ patients, but only 2.6% and 4.6% in SARC- patients (Fig. 2). Adding genotype to the HCM SCD risk calculator resulted in a net reclassification index of 0.162 (0.046–0.285). Compared with the original model and using a 6%/5-year cutoff, the new model identified 8 more patients with events as high-risk but also an additional 4 patients without events. Conclusion: In our cohort, both FHxSCD and genotype were independently associated with SCD outcomes. In patients with FHxSCD as the sole risk marker, the risk of SCD is significant (>6%/5 years) in SARC+ patients but not in SARC- patients. Adding genotype to the HCM SCD risk calculator improves its ability to discriminate between patients at high and low risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".